Leif Nixon <nixon at nsc.liu.se> wrote:
> Have you looked at RFC3797? Not sure if it has any solutions for you,
but it
> at least discusses the same problems.
Good reference, I was not aware of that.
It gives the same sorts of sources for random numbers as we have come up
with here: stock market, sports, lottery. It discusses how stock market
data may not be reliable due to market splits and other accounting
issues. However, I have determined that the raw data from the exchanges
is a terrible choice because it is not available for free, and the
values that are freely available, which are posted on web finance sites,
are not reliably identical in all digits.
Lottery results are a good source except for the black box / black
helicopter factors. We don't generally know where those numbers are
coming from, and even in those cases where they do tell us, there is no
way to verify that any particular lottery drawing wasn't rigged.
We have not discussed election results (votes per candidate), but those
are, ironically, really unsuitable for this, even though statistically
the final set of digits should have a lot of entropy. Mostly election
numbers are a problem because they may be revised for long periods after
the election, and the numbers could almost always be forced to shift by
a challenge by one of the candidates. Every recount will come up with a
slightly different result. Examples: the Coleman vs. Franken senatorial
contest in Minnesota, or Bush vs. Gore in Florida.
So I'm leaning towards sports scores, as those are generated in full
view of a multitude of witnesses (often numbering in the millions). It
would be extremely difficult to rig the absolute final score. It might
be possible to rig the winner, or even the point spread, but to rig the
absolute score in a high scoring game like basketball, would be
exceedingly difficult, and would likely be obvious to even the casual
observer. To rig every digit in the final score of every game played on
a given day should be pretty close to impossible.
Regards,
David Mathog
mathog at caltech.edu
Manager, Sequence Analysis Facility, Biology Division, Caltech